The use of ARIMA, ANN and SVR models in time series hybridization with practical application

Document Type : Research Paper


College of Administration and Economics, Department of Statistics, University of Baghdad, Iraq


Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices.  The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the prediction process when conducting simulations for the time series and for different sample sizes and when applying them on the daily crude oil price data, it was more efficient than the single models, as the comparison between the single models and the proposed hybrid models was done by means of the comparison scale, the mean square error (MSE), the results showed that the proposed hybrid models gave more accurate and efficient results, in addition to its ability to predict crude oil prices well.


[1] M.A. Ashour, Artificial neural networks and time series prediction methods, 1st ed., Memory for Publishing and Distribution, 2018.
[2] A. G¨okhan, C.O. G¨uzeller, and M.T. Eser, The effect of the normalization method used in different sample sizes on the success of artificial neural network model, Int. J. Assess. Tools Educ. 6 (2019), no. 2, 170–192.
[3] X. Hu, Support vector machine and its application to regression and classification, MSU Graduate Theses, Missouri State University, 2017.
[4] A.H. Kadim, Introduction to economic measurement, 2009.
[5] G. Khalaf and M. Iguernane, Ridge Regression and Ill-Conditioning, J. Mod. Appl. Statist. Meth. 13 (2014), no. 2, 355–363.
[6] D.C. Montgomery, C.L. Jennings, and M. Kulahci, Introduction to time series analysis and forecasting, John Wiley & Sons, 2015.
[7] H. Mori and D. Kanaoka, Application of support vector regression to temperature forecasting for short-term load forecasting, Int. Joint Conf. Neural Networks, IEEE, 2007, pp. 1085–1090.
[8] A. Sadiq, Intelligent systems and machine learning, Al-thakera, Baghdad, 2016.
[9] A.T. Sadiq, Machine learning methods and algorithms, Al-thakera, Baghdad, 2021.
[10] S. Sivanandam, Introduction to artificial neural networks, Vikas Publishing House, 2009.
Volume 14, Issue 3
March 2023
Pages 87-102
  • Receive Date: 24 August 2022
  • Revise Date: 18 October 2022
  • Accept Date: 20 November 2022